#!/usr/bin/env python3
"""模式合并视图等价验证（零 API）：
  A. 三通道组装计数（clean 441 / injected 35 / sweep 58 = 534）
  B. adversarial/variant/robustness 视图函数 vs 存档模式报告逐键对比（应精确一致）
  C. attribution 视图重放 vs 0731 真实 attribution（top1 一致即过，历史已证等价）
"""
import glob
import json
import sys

from pathlib import Path  # noqa: E402

sys.path.insert(0, str(Path(__file__).resolve().parents[2]))  # 仓库根/site-packages, 使 evalharness 包可导入
from evalharness.fingerprint.attribution import family_attribution
from evalharness.fingerprint.battery import CORE16_CELLS, TEXT_PRUNED_V7
from evalharness.fingerprint.probes_adv import adversarial_signal
from evalharness.fingerprint.probes_variant import variant_signal
from evalharness.fingerprint.run_fp_fusion import _assemble_probes
from evalharness.fingerprint.scorer import load_aliases, requested_family, robustness_signal

BFD = "/tmp/bfd"
ROLE_KIMI = ("You are Kimi, Moonshot AI virtual assistant designed by "
             "Moonshot AI. You are Kimi.")
aliases = load_aliases(None)


def load(p):
    return [json.loads(l) for l in open(p)]


def diff(a, b, path=""):
    out = []
    if isinstance(a, dict) and isinstance(b, dict):
        for k in set(a) | set(b):
            out += diff(a.get(k), b.get(k), f"{path}.{k}")
    elif isinstance(a, (int, float)) and isinstance(b, (int, float)) \
            and not isinstance(a, bool) and not isinstance(b, bool):
        if abs(a - b) > 1e-6:
            out.append((path, a, b))
    elif a != b:
        out.append((path, a, b))
    return out


# ---------- A. 组装计数 ----------
skip = set(TEXT_PRUNED_V7)
p1 = _assemble_probes("variant", None, skip)
p2 = _assemble_probes("adversarial", ROLE_KIMI, skip)
p3 = _assemble_probes("verify", None, skip)
total = 16 * 25 + 5 + len(p1) + len(p2) + len(p3) * 2
print(f"A. 三通道: clean={16 * 25 + 5 + len(p1)}(D400+基线5+文本V{len(p1)}) "
      f"injected={len(p2)} sweep={len(p3)}×2={len(p3) * 2} | 合计 {total} (期望534)")

# ---------- B. 三视图精确对比 ----------
req = requested_family("ZhipuAi/GLM-5.3", aliases)

adv_recs = load(f"{BFD}/glm_53/adv/raw_answers.jsonl")
adv_json = json.load(open(glob.glob(f"{BFD}/glm_53/adv/*.json")[0]))
mine = adversarial_signal([r for r in adv_recs if r.get("layer") == "ADV"],
                          all_records=adv_recs, requested_family=req,
                          dist_family=req, aliases=aliases, mode="adversarial",
                          impersonate_role=ROLE_KIMI)
d = diff(mine, adv_json["signals"]["adversarial"])
print(f"B1. adversarial 视图 vs 存档: {'精确一致 ✓' if not d else d[:5]}")

var_recs = load(f"{BFD}/glm_53/var/raw_answers.jsonl")
var_json = json.load(open(glob.glob(f"{BFD}/glm_53/var/*.json")[0]))
mine = variant_signal(var_recs, logprobs_enabled=True)
stored = {k: v for k, v in var_json["signals"]["variant"].items() if k != "notes"}
mined = {k: v for k, v in mine.items() if k != "notes"}
d = diff(mined, stored)
print(f"B2. variant 视图 vs 存档: {'精确一致 ✓' if not d else d[:5]}")

rob_recs = load(f"{BFD}/glm_53/rob/raw_answers.jsonl")
rob_json = json.load(open(glob.glob(f"{BFD}/glm_53/rob/*.json")[0]))
mine = robustness_signal(rob_recs, temperature_sweep=[0.0, 0.7, 1.0])
d = diff(mine, rob_json["signals"]["robustness"])
print(f"B3. robustness 视图 vs 存档: {'精确一致 ✓' if not d else d[:5]}")

# ---------- C. attribution 视图 ----------
recs = load(f"{BFD}/deepseek_v4_flash_0731/raw_answers.jsonl")
real = json.load(open(f"{BFD}/deepseek_v4_flash_0731/attr_real/attribution_real.json"))
req0731 = requested_family("DeepSeek/DeepSeek-V4-Flash-0731", aliases)
mine = family_attribution(recs, aliases=aliases, requested_family=req0731,
                          llmmap_tool=None)
sf = real["signals"]["family"]
print(f"C. attribution 视图: 真跑 top1={sf.get('top1_family')} conf={sf.get('confidence'):.3f} | "
      f"重放 top1={mine.get('top1_family')} conf={mine.get('confidence'):.3f} | "
      f"top1 一致 {'✓' if mine.get('top1_family') == sf.get('top1_family') else '✗'}")
print("（conf 差异源于真跑启用 LLMmap 辅路投票，离线推导等价性此前已在 derive_attribution 验证）")